The {BLCOP} package is an implementation of the
Black-Litterman and copula opinion pooling frameworks. The
Black-Litterman model was devised in 1992 by Fisher Black and Robert
Litterman. Their goal was to create a systematic method of specifying
and then incorporating analyst/portfolio manager views into the
estimation of market parameters.
BLViews() and COPViews() construct views
objectsaddBLViews() and addCOPViews() allow more
views to be added to existing objectsdistribution() and mvdistribution() create
distribution and mvdistribution objectsBLPosterior() calculates the posterior distribution
using the Black-Litterman modelCOPPosterior() calculates the posterior distribution
using copula opinion poolingYou can install the released version of BLCOP from CRAN with:
install.packages("BLCOP")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("MangoTheCat/BLCOP")library(BLCOP)
# For a matrix of monthly returns for 6 assets
head(monthlyReturns)
#> IBM MS DELL C JPM BAC
#> 1998-02-02 0.057620253 0.19578623 0.40667739 0.1224778047 0.157384084 0.143954576
#> 1998-03-02 -0.005457679 0.04383326 -0.51565628 0.0785547367 0.087215863 0.064817518
#> 1998-04-01 0.115529027 0.08233841 0.19188192 0.0198333333 0.027283511 0.041952290
#> 1998-05-01 0.014067489 -0.01027006 0.02055728 0.0009805524 -0.018908776 -0.006578947
#> 1998-06-01 -0.022893617 0.17050986 0.12619828 -0.0101224490 -0.444607915 0.015761589
#> 1998-07-01 0.154080655 -0.04717084 0.17002478 0.1091868712 0.001589404 0.039900900
# Define a pick matrix (a vector of confidences)
pickMatrix <- matrix(c(1/2, -1, 1/2, 0, 0, 0),
nrow = 1,
ncol = 6)
# Create a views object
views <- BLViews(P = pickMatrix,
q = 0.06,
confidences = 100,
assetNames = colnames(monthlyReturns))
# Determine the posterior distribution of these assets
BLPosterior(monthlyReturns, views, tau = 1/2, marketIndex = sp500Returns)
#> Prior means:
#> IBM MS DELL C JPM BAC
#> 0.002269870 0.005799591 -0.001161339 0.001718354 -0.009042287 0.005472691
#> Posterior means:
#> IBM MS DELL C JPM BAC
#> 0.009795730 -0.016744179 0.014453759 -0.004741680 -0.015465517 0.001505639
#> Posterior covariance:
#> IBM MS DELL C JPM BAC
#> IBM 0.022113337 0.011762652 0.013388809 0.009418743 0.01189892 0.006017050
#> MS 0.011762652 0.033040555 0.018441735 0.014076656 0.01650328 0.009143918
#> DELL 0.013388809 0.018441735 0.048344919 0.008453909 0.01088555 0.005957519
#> C 0.009418743 0.014076656 0.008453909 0.017307957 0.01246270 0.007215142
#> JPM 0.011898924 0.016503281 0.010885549 0.012462701 0.03032755 0.012937189
#> BAC 0.006017050 0.009143918 0.005957519 0.007215142 0.01293719 0.011893184